The system relates to machine determination of periodic event behaviors.
A periodic event is an event that happens regularly over and over again at a fixed interval or a set of fixed intervals (meaning the time between events is substantially the same or among several possible values). Periodicity analysis from the recorded log data is an important task which provides useful insights into the physical events and enables a system to report outliers and predict future behaviors. For example, FIG. 1 shows a set of text logs mined from IT system logs including the periodical pattern of a system event type: it happens twice a day, around 1:00 AM and 14:00 PM. Based on the log event periodicity learnt from FIG. 1, unexpected system behaviors can be detected. For example, FIG. 2 shows the detection of two events that violate the 1:00 AM and 14:00 PM periodicity.
To mine periodicity in an event, systems have to face real-world challenges of inherently complicated periodic behaviors and imperfect data collection problem. Specifically, the hidden temporal periodic behaviors could be oscillating and noisy. Traditional periodicity analysis methods, such as Fourier transform (FFT) and auto-correlation usually require the data to be evenly sampled, that is, there is an observation at every timestamp. Even though some extensions of Fourier transform have been proposed to handle uneven data samples, they are still not applicable to the case with very low sampling rate.
Some methods apply statistical analysis techniques on a single time series of one event type. A probabilistic measure for periodicity, ePeriodicity, has been used to detect periods. This is done by applying different potential periodicity length T to segment the time series into multiple length-T time series, overlay those time series, and report as the periodicity the value T that have the largest clustering behavior measured by an event conditional probability.